# How to Build a Price Monitoring Agent with Pydantic AI

Keeping track of fluctuating product prices across e-commerce platforms can be a daunting task.

Whether you're tracking a personal wishlist or monitoring competitors' pricing for your business, automating this process can save time and effort.

In this guide, we’ll explore how to build a **price monitoring agent** using the **Pydantic AI framework**—a robust agentic framework from the creators of Pantic, a popular data validation library.

![](https://cdn.hashnode.com/res/hashnode/image/upload/v1734328934789/a46830a9-7c76-4205-a503-50bba31bcdbf.png align="center")

This tutorial is part one of a series. Today, we’ll focus on building a scraper agent to extract key product details like title, description, price, and more.

In the next part, we’ll expand this agent to store data in a database and send notifications for price changes.

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## What is Pydantic AI?

[Pydantic AI](https://ai.pydantic.dev/) is revolutionizing the way developers build applications that leverage Generative AI. As a Python Agent Framework, it simplifies the creation of production-grade applications by integrating robust data validation with the power of LLMs. Here’s why Pydantic AI stands out:

* **Built on Proven Foundations**: Developed by the creators of Pydantic, which is widely used in various AI frameworks like OpenAI and LangChain, Pydantic AI inherits a strong legacy of type safety and structured data management.
    
* **Model-Agnostic Flexibility**: Currently supporting models like OpenAI, Gemini, and Groq, Pydantic AI allows developers to easily implement support for additional models through a simple interface. This flexibility ensures that your application can adapt to various AI technologies without significant overhead.
    
* **Enhanced Developer Experience**: With features like vanilla Python control flow and a novel dependency injection system, Pydantic AI empowers developers to apply familiar coding practices. This leads to more maintainable code and a smoother development process.
    
* **Streamlined Response Validation**: The framework not only validates incoming data but also ensures that responses from LLMs are structured and validated, enhancing reliability in application behavior.
    

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## Overview of the Price Monitoring Agent

Our agent will:

1. Scrape product details (title, description, price, currency, and image URL) from a given URL.
    
2. Parse the information into a structured format.
    
3. Prepare for database storage and notification handling (to be implemented in part two).
    

Here’s how the process works (diagram above)

1. **Input**: Product page URL
    
2. **Scraper Tool**: Extracts structured data using Beautiful Soup and Markdownify.
    
3. **Agent**: Processes the scraped data using Pydantic AI for type-safe responses.
    

## Watch the video for full tutorial

%[https://www.youtube.com/watch?v=hlropi13fO8]